Water cover is one of the basic parameters of water cycle and energy balance. Based on the AVHRR daily reflectance time series from 1982 to 2020, this data set has produced 39 year long-term daily water body mapping products (including water body icing information) on the Qinghai Tibet Plateau. This dataset contains 39 folders, named after the year (from 1982 to 2020). Each folder contains 365 / 366 GeoTIFF files, and each file contains two bands: (1) water mapping band (waterlayer); (2) Quality control information band (QC). This product provides data support for remote sensing monitoring of water bodies in the Qinghai Tibet Plateau.
JI Luyan
The dataset contains the continuous daily lake surface temperature of 160 Lakes (with an area of more than 40km2) in the Tibetan Plateau from 1978 to 2017. Firstly, an semi-physical lake model (air2water) based on energy balance was improved to realize the continuous simulation of lake surface temperature even during ice age. The impoved model was calibrated by lake surface temperature from MOD11A1 product. The correlation between the dataset and in-situ lake surface temperature of four lakes is higher than 0.9, and the root mean square errors are less than 2.5 ℃. The data set provides data support for understanding the water and heat balance , the process of aquatic ecosystem and its response to climate change of lakes in the Tibetan Plateau.
GUO Linan , WU Yanhong, ZHENG Hongxing , ZHANG Bing , WEN Mengxuan
This data set was collected in 2018 during the ground-based microwave radiometry and radar cooperative experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Zhenglan Banner, Inner Mongolia (115.93° E, 42.04° N, at 1362 m in altitude). The data set contains four parts, namely brightness temperature data, radar backscatter coefficient, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data was acquired every 30 minutes from 30° to 65° with an interval of 2.5°. The active microwave data is obtained by ground-based synthetic aperture radar (GBSAR), including the L- and C-band backscattering coefficients under four polarization modes (VV, VH, HH, HV), and the incidence varies from 30° to 65° (2.5° interval). The soil data contains the surface roughness, soil moisture and temperature at six depths of layer (1 cm, 3 cm, 5 cm, 10 cm, 20 cm, 50 cm). The vegetation data is mainly the vegetation water content of the grassland. The experimental period lasted from August 18 to September 25, 2018, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.
ZHAO Tianjie, HU Lu, GENG Deyuan, SHI Jiancheng
Water clarity, as a first-order indicator that reflects the optical characteristics of water bodies, represents a comprehensive proxy for aquatic ecosystems’ trophic state. Optical remote sensing technology makes it possible to monitor water clarity changes of lakes (including reservoirs) at large scales. Water clarity annual dynamics dataset of lakes (>1 ha) across China covers the period from 1990 to 2018, with a time resolution of 5-year and spatial resolution of 30 meters, which sources from the Landsat top of air reflectance data embedded in the GEE platform. Three in-situ SDD measurement datasets were used for model calibration and validation. The first dataset was obtained from 37 field campaigns by our team during 2004-2018. Three quarters of this dataset (N= 976) were used to calibrate the model, for which the R2 and rRMSE were 0.79 and 61.9%, respectively; the remaining dataset (N= 325) was used to validate the model, and the validation results indicated stable performance by showing comparative errors (R2=0.80, rRMSE = 57.6%). The second and the third datasets were both used to validate model performance with a major focus on testing the temporal transferability of the model. The second dataset (340 samples), collected as part of the Chinese lakes survey conducted by Nanjing Institute of Geography and Limnology from 2007 to 2009, also indicated a good model performance (R2=0.78, rRMSE% = 59.1%); the third dataset (229 samples) was assembled by the first lake surveys conducted in the 1980s, demonstrating a stable performance for lake SDD before 1990s (R2=0.81, rRMSE = 50.6%). Comparison of validation results for these different periods and datasets demonstrated the stable performance of the SDD model. Finally, based on the water clarity estimation model, the algorithms of cloud mask and water index were conducted on the GEE platform to accomplish the water clarity of lakes across China. The water clarity information could assist local, provincial or even national level decision-making on policies/management for protecting or improving inland water quality.
TAO Hui, SONG Kaishan, LIU Ge, WANG Qiang, WEN Zhidan
This data set was collected in summer 2017 during the ground-based microwave radiometry experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Duolun County, Inner Mongolia (116.47°E, 42.18°N, at 1269 m in altitude). The data set contains three parts, namely brightness temperature data, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data were acquired from 30° to 65° with an interval of 2.5°, and the time resolution is 0.5 hours. Soil data contains 5 layers of soil moisture and soil temperature (2.5 cm, 10 cm, 20 cm, 30 cm, 50 cm) over three croplands (corn, oats, and buckwheat), with sampling intervals of 10 minutes. The soil data also contains soil surface roughness, rainfall, irrigation flags, and soil texture. Vegetation data contains leaf area index, plant height, vegetation water content, etc. The experimental period lasted from July 19 to August 30, 2017, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.
ZHAO Tianjie, HU Lu, LI Shangnan, FAN Dong, WANG Pingkai, GENG Deyuan, SHI Jiancheng
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
WANG Junbo
This is the 1976, 1991, 2000, and 2010 vector data set of glaciers and glacial lakes in the Boqu Basin in Central Himalaya based on Landsat satellite images. The data source is from Landsat remote images. 1976: LM21510411975306AAA05, LM21510401976355AAA04 1991: LT41410401991334XXX02, LT41410411991334XXX02 2000: LE71410402000279SGS00, LE71400412000304SGS00, LE71410402000327EDC00, LE71410412000327EDC00 2010: LT51400412009288KHC00, LT51410402009295KHC00, LT51410412009311KHC00, LT51410402011237KHC00. The boundaries of glaciers and glacial lakes are extracted manually from the various remote sensing images. The extraction error of the boundaries of glaciers and glacial lakes is estimated to be 0.5 pixels. Data file: Glacial_1976: Glacier vector data in 1976 Glacial_1991: Glacier vector data in 1991 Glacial_2000: Glacier vector data in 2000 Glacial_2010: Glacier vector data in 2010 Glacial_Lake_1976: Glacial lake vector data in 1976年 Glacial_Lake_1991: Glacial lake vector data in 1991 Glacial_Lake_2000: Glacial lake vector data in 2000 Glacial_Lake_2010: Glacial lake vector data in 2010 The glacial lake vector data fields include Number, name, latitude and longitude, altitude, area, orientation, type of glacial lake, length, width, and distance from the glacier.
WANG Weicai
The data set of lake dynamics on the Tibetan Plateau was mainly derived from Landsat remote sensing data. Band ratio and the threshold segmentation method were applied. The temporal coverage of the data set was from 1984 to 2016, with a temporal resolution of 5 years. It covered the whole Tibetan Plateau at a spatial resolution of 30 meters. The water body area extraction method mainly adopted the band ratio (B4/B2) or water body index to construct the classification tree. The algorithm construction considered the spatial and temporal variations of the spectral characteristics of the water body and adjusted the threshold of the decision tree by the slope and the slope aspect information of the water body. The long-term sequence satellite-borne data came from different sensors, e.g., Landsat MSS, TM, ETM+, and OLI. The minimum unit for extracting water body information was 2*2 pixels, and all water body areas less than 0.36*10^-2 Km² were removed. The water body information extracted by high-resolution remote sensing data and the verification of the water body checkpoint determined by visual interpretation indicated that the overall accuracy of the water body area information for the Tibetan Plateau was above 95%. The data were saved as a shape file, and projected by Albers projection, with a central meridian of 105 ° and a double standard latitude of 25 ° and 47 °.
SONG Kaishan, DU Jia
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